ABSTRACT Machine-learning techniques have found widespread applications in bioinformatics. Such techniques provide invaluable insight on understanding the complex biomedical mechanisms and predicting the optimal individualized intervention for patients. In our case, we are particularly interested in developing an individualized clinical guideline on wheelchair tilt and recline usage for people with spinal cord injury (SCI). The current clinical practice suggests uniform settings to all patients. However, our previous study revealed that the response of skin blood flow to wheelchair tilt and recline settings varied largely among patients. Our finding suggests that an individualized setting is needed for people with SCI to maximally utilize the residual neurological function to reduce pressure ulcer risk. In order to achieve this goal, we intend to develop an intelligent model to determine the favorable wheelchair usage to reduce pressure ulcers risk for wheelchair users with SCI. In this study, we use artificial neural networks (ANNs) to construct an intelligent model that can predict whether a given tilt and recline setting will be favorable to people with SCI based on neurological functions and SCI injury history. Our results indicate that the intelligent model significantly outperforms the traditional statistical approach in accurately classifying favorable wheelchair tilt and recline settings. To the best of our knowledge, this is the first study using intelligent models to predict the favorable wheelchair tilt and recline angles. Our methods demonstrate the feasibility of using ANN to develop individualized wheelchair tilt and recline guidance for people with SCI.

[Show abstract][Hide abstract]ABSTRACT:
The ion flow structure in a diverging magnetic field is measured in a steady-state electron cyclotron resonance plasma. It has been observed that stream line detachment takes place when the nonadiabaticity parameter of ions becomes the order of unity. In the detachment region, the plasma starts an azimuthal rotation, and the energy conservation given by the 1-D model is no longer applicable. Index Terms—Fluid flow measurement, plasma properties.

[Show abstract][Hide abstract]ABSTRACT:
People with spinal cord injury (SCI) are at risk for pressure ulcers because of their poor motor function and consequent prolonged sitting in wheelchairs. The current clinical practice typically uses the wheelchair tilt and recline to attain specific seating angles (sitting postures) to reduce seating pressure in order to prevent pressure ulcers. The rationale is to allow the development of reactive hyperemia to re-perfuse the ischemic tissues. However, our study reveals that a particular tilt and recline setting may result in a significant increase of skin perfusion for one person with SCI, but may cause neutral or even negative effect on another person. Therefore, an individualized guidance on wheelchair tilt and recline usage is desirable in people with various levels of SCI. In this study, we intend to demonstrate the feasibility of using machine-learning techniques to classify and predict favorable wheelchair tilt and recline settings for individual wheelchair users with SCI. Specifically, we use artificial neural networks (ANNs) to classify whether a given tilt and recline setting would cause a positive, neutral, or negative skin perfusion response. The challenge, however, is that ANN is prone to over fitting, a situation in which ANN can perfectly classify the existing data while cannot correctly classify new (unseen) data. We investigate using the genetic algorithm (GA) to train ANN to reduce the chance of converging on local optima and improve the generalization capability of classifying unseen data. Our experimental results indicate that the GA-based ANN significantly improves the generalization ability and outperforms the traditional statistical approach and other commonly used classification techniques, such as BP-based ANN and support vector machine (SVM). To the best of our knowledge, there are no such intelligent systems available now. Our research fills in the gap in existing evidence.

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The purpose of this work was to review the applications of ANN, Artificial Neural Networks, in the pharmaceutical research, drug delivery systems, and pharmacy curriculum. With the advent of the computers and their applications in biosciences, significant changes are under way in the research processes and it is crucial for the research laboratories and pharmacy schools to be aware of the benefits of bioinformatics methods such as ANNs. Literature survey was conducted to assess the scope of applications of ANNs in the pharmaceutical research and the ability of ANNs to provide new areas of professional opportunities to the Pharmacy students. Our literature survey results indicated that ANNs can be very useful in many aspects of pharmaceutical research including pharmacokinetics and pharmacodynamics modeling, optimization of dosage and drug delivery systems. ANNs can be taught as a part of the PharmD (Doctor of Pharmacy) curriculum to equip the students for quick and effective formulation design and optimization of pharmaceutical doses. In this work, we have successfully summarized the applications of ANNs in pharmaceutical research and found that ANNs play an increasingly important role in pharmaceutical research and education.

Biomedical Engineering Conference (SBEC), 2013 29th Southern; 01/2013

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Development of Intelligent Model to Determine FavorableWheelchair Tilt and Recline Angles for People with Spinal CordInjuryJicheng Fu,University of Central Oklahoma, Edmond, OK 73034 USAYih-Kuen Jan, andUniversity of Oklahoma Health Sciences Center, Oklahoma City, OK 73117 USAMaria JonesUniversity of Oklahoma Health Sciences Center, Oklahoma City, OK 73117 USAAbstractMachine-learning techniques have found widespread applications in bioinformatics. Suchtechniques provide invaluable insight on understanding the complex biomedical mechanisms andpredicting the optimal individualized intervention for patients. In our case, we are particularlyinterested in developing an individualized clinical guideline on wheelchair tilt and recline usagefor people with spinal cord injury (SCI). The current clinical practice suggests uniform settings toall patients. However, our previous study revealed that the response of skin blood flow towheelchair tilt and recline settings varied largely among patients. Our finding suggests that anindividualized setting is needed for people with SCI to maximally utilize the residual neurologicalfunction to reduce pressure ulcer risk. In order to achieve this goal, we intend to develop anintelligent model to determine the favorable wheelchair usage to reduce pressure ulcers risk forwheelchair users with SCI. In this study, we use artificial neural networks (ANNs) to construct anintelligent model that can predict whether a given tilt and recline setting will be favorable topeople with SCI based on neurological functions and SCI injury history. Our results indicate thatthe intelligent model significantly outperforms the traditional statistical approach in accuratelyclassifying favorable wheelchair tilt and recline settings. To the best of our knowledge, this is thefirst study using intelligent models to predict the favorable wheelchair tilt and recline angles. Ourmethods demonstrate the feasibility of using ANN to develop individualized wheelchair tilt andrecline guidance for people with SCI.I. IntroductionPressure ulcers significantly affect the quality of life of wheelchair users with SCI. Pressureulcers have become the second cause of rehospitalization for people with SCI [4]. It isestimated that more than 50% of people with SCI will develop at least one pressure ulcer intheir lifetime [13]. Annual U.S. treatment costs of pressure ulcers in people with SCI areapproximately $1.3 billion, accounting for 25% of the total cost of treating SCI [3]. It isclear that research regarding the prevention of pressure ulcers remains a priority in peoplewith SCI.The current clinical practice uses wheelchair power seat function (PSF) to adjust tilt (achange of seat angle orientation while maintaining the seat-to-back angle) and recline (achange of the seat-to-back angle) to reduce seating interface pressure to prevent pressureulcers. The principle of wheelchair tilt and recline is based on the evidence that turning thepatient every 2 hours results in a lower incidence of pressure ulcers [15]. Sitting-inducedNIH Public AccessAuthor ManuscriptConf Proc IEEE Eng Med Biol Soc. Author manuscript; available in PMC 2012 February 21.Published in final edited form as:Conf Proc IEEE Eng Med Biol Soc. 2011 August ; 2011: 2045–2048. doi:10.1109/IEMBS.2011.6090377.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscript

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pressure could be relieved by performing wheelchair tilt and recline [10]. Generally, there isa consensus regarding the use of tilt and recline to reduce seating interface pressure;however, the recommended usage of tilt and recline differs among clinicians and facilities[5].To determine the efficacy of seating conditions to reduce the pressure ulcers risk, skin bloodflow response to loading pressure has been regarded as an accurate way [9][10]. Reactivehyperemia is a transient increase in skin blood flow after ischemia [2]. Both the magnitudeand duration of the reactive hyperemia have been shown to relate to the magnitude andduration of the external loads [2]. The purpose of periodically performing pressure-relievingactivities (e.g. tilt and recline usage) is to allow the development of reactive hyperemia to re-perfuse the ischemic tissues [10]. Inadequate blood flow increase to ischemic tissues maylead to pressure ulcers [14]. However, at what angle wheelchair tilt and recline usageprovides adequate pressure relief for enhancing skin blood flow and soft tissue viability isnot clear [9].We performed a study to investigate the effectiveness of wheelchair tilt and recline onenhancing skin perfusion in 11 wheelchair users with SCI [9]. The main factors include thecommonly used tilt and recline angles, including tilt at 15°, 25°, and 35° and recline at 100°and 120°. A combination of 3 tilt and 2 recline angles resulted in 6 testing conditions. Basedon the average skin perfusion on each testing condition, we found that as the angles of tiltand recline increase, the average skin perfusion also increases. Although this pattern workswell in general, we found that it did not work on some individual cases, in which theincrease of tilt and recline angles resulted in decrease of the skin perfusion. In fact, using theaverage data to classify wheelchair tilt and recline settings shares the same weakness as thecurrent clinical practice that provides uniform guidance on wheelchair tilt and recline usageto patients with SCI. Therefore, it is highly desirable to develop an intelligent system thatcan predict the favorable wheelchair usage to reduce pressure ulcers risk for individualwheelchair users.Machine-learning techniques can capture characteristics of interests based on examples (i.e.,training data) even though the underlying nature, principles, and/or probability distributionsare unclear. As a result, machine-learning techniques are well suited in this study becausemany factors, such as level of injury, completeness, duration of injury, etc., may interactwith each other to affect skin perfusion. However, the nature and principles with regard tohow these factors interact remain unknown [6]. In this study, we use artificial neuralnetwork (ANN) to construct an intelligent model that considers multiple factors and is ableto predict whether a tilt and recline setting would increase skin perfusion for individualwheelchair users with SCI. ANN is a powerful computational model with many appealingproperties, such as learning capability, adaptability, and ability to generalize [1]. All theseproperties are desirable in this study.To the best of our knowledge, no such intelligent models are currently available. Hence, thegoals of this study are to (1) demonstrate the feasibility of using machine-learningtechniques to construct such an intelligent model; and (2) investigate methods to determinethe attributes relevant to skin perfusion and build the intelligent model based on the relevantattributes. The experience learned from this study will benefit investigators in this area.The rest of the paper is organized as follows. In Section II, we present the methods used inthis study. Then, we show the experimental results in Section III, present the discussion inSection IV, and conclude in Section V.Fu et al.Page 2Conf Proc IEEE Eng Med Biol Soc. Author manuscript; available in PMC 2012 February 21.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscript

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II. METHODSWe performed a study [9] to investigate the blood flow response to wheelchair tilt andrecline usage in 11 wheelchair users with SCI. The main factors include the commonly usedtilt and recline angles, including tilt at 15°, 25°, and 35° and recline at 100° and 120°. Thefactorial design created 6 testing conditions (A1B1, A2B1, A3B1, A1B2, A2B2, and A3B2) asshown in Table I. The order of the 6 testing conditions was randomly assigned to thesubjects. Skin blood flow was continuously measured through the experiment. Eachcondition lasted for 10 minutes. The first 5-minute was the sitting-induced ischemic period(no tilt or recline). The skin perfusion b0 was measured during the ischemic period. The next5-minute was the pressure reduction period caused by performing wheelchair tilt and recline,during which the skin perfusion b1 was measured. The skin perfusion increase wascomputed by the ratio:(1)In addition, the subject assumed a sitting posture of 35 degree tilt and 120 degree recline fora duration of 5 minutes to restore blood flow supply to ischemic tissues between eachconditions [10]. Each subject spent 90 minutes to complete the experimental protocol. 11participants with 6 testing conditions produced 66 skin perfusion data.A. Traditional Statistical AnalysisWe used traditional statistical approach to analyze skin blood flow response to wheelchairtilt and recline usage based on the average skin perfusion increase ratio β̄ on each testingcondition. The averaged data demonstrates a strong pattern: as the angles of tilt and reclineincrease, the average skin perfusion increase ratio β̄ also increases. The wheelchair tiltshould be at least 35° for enhancing skin perfusion over the ischial tuberosity whencombined with recline at 100° and should be at least 25° when combined with recline at120° [9].Although the above pattern works well in general, we found that it did not work on someindividual cases, in which the increase of tilt and recline angles resulted in decrease of skinperfusion. We used the average skin perfusion ratio β̄ to classify data in the same testingcondition. Specifically, if β̄ > 1 on a particular testing condition (i.e., a particular tilt andrecline setting), then we classify all the data on this testing condition as positive. On theother hand, if β̄ <= 1, all the data on this testing condition is classified as negative. Based onthis method, the classification accuracy rate is only 59.38%. Therefore, the traditional wayto investigate blood flow response to wheelchair tilt and recline usage is not satisfying.B. Using ANN to Study Blood Flow Response to Wheelchair Tilt and Recline UsageSince no such intelligent models are currently available, there is no previous experience tofollow. In this study, we explore methods to determine the attributes relevant to skinperfusion and, then, build the intelligent model based on the relevant attributes.Specifically, we want to determine a function f(a1, a2, …, ak, t, r) → {0, 1}, where a1, a2,…, ak are attributes (or factors) of participants, such as level of injury, duration of injury,etc, and t and r are a particular tilt and recline setting. The purpose of the function f is thatgiven a patient modeled with attributes 〈a1, a2, …, ak〉, the function f will determine whetherthe tilt and recline setting 〈t, r〉 will result in skin perfusion increase (denoted by 1;otherwise, 0).Fu et al. Page 3Conf Proc IEEE Eng Med Biol Soc. Author manuscript; available in PMC 2012 February 21.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscript

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To determine the function f, we need to (1) prepare training data for machine-learningalgorithms; (2) determine the set of attributes {a1, a2, …, ak} that is relevant to skinperfusion; and (3) establish an intelligent model based on the relevant attribute set such thatfunction f can accurately classify existing and unseen data.1) To prepare training data—We collected participants’ attributes that are reported to berisk factors for pressure ulcers, including age (a), gender (g), duration of injury (d), level ofinjury (l), and completeness (c) [6][7]. The reason that we also consider demographicattributes is that SCI individuals with certain demographic attributes may be morevulnerable to pressure ulcers [6]. With exiting information, we are able to derive anadditional attribute, namely, age at onset of SCI (o) with o = a − d. Combining all theattributes together, we obtain a raw model for a participant as:(2)where P is the set of participants and a, g, d, l, c, and o are attributes defined as above. Then,the set of raw data is defined as:(3)where P is the set of participants defined in (2); Γ is the set of tilt and recline settings; and βis the skin perfusion increase ratio defined in (1).Based on D, we prepare the training data for attribute selection and classification algorithms.For any data 〈a, g, d, l, c, o, t, r, β〉 ∈ D, it is transformed into an example pair (〈a, g, d, l, c,o, t, r〉, y), where 〈a, g, d, l, c, o, t, r〉 ∈ P × Γ; y = 1 iff β > 1 and, otherwise, y = 0. The dataitem 〈a, g, d, l, c, o, t, r〉 serves as the input to the machine-learning algorithms and y is theexpected output. Then, all the training data is put into a set X as follows:(4)2) To determine the relevant attributes—We take two steps to determine a subset ofthe attributes that is relevant to skin perfusion from the raw training data X (defined in (4)).In the first step, we use correlation-based feature subset selection (CFS) algorithm [8] toobtain a set of relevant attributes. CFS is a state-of-the-art attribute selection algorithm andis highly ranked in attribute selection repository [17]. We call the set of attributes returnedfrom CFS as the core attributes set. This core set, however, may miss some relevantattributes. Hence, in the second step, we gradually add the remaining attributes to the coreset, one attribute at a time. Each time when an attribute is added to the core set, we use ANNto construct the function f based on the new core attributes set.3) To establish the intelligent model by using ANN—Artificial neural network(ANN) provides a general and practical method for learning functions from examples(training data). An ANN consists of a set of processing units (neurons) that communicateamong themselves by sending signals. The signals travel through weighted connectionsbetween neurons. Upon receiving signals, these neurons accumulate the inputs and produceoutputs according to their internal activation functions. The outputs can serve as inputs forother neurons, or can be a part of the network outputs [12]. Learning is achieved throughadjusting the weights of connections between neurons.Specifically, we use two approaches to build function f and examine its generalizationability. (1) We use all the data to train ANN and use the same set of data to test how well thelearned function f classifies these data. We call this approach as “train and test with the sameFu et al. Page 4Conf Proc IEEE Eng Med Biol Soc. Author manuscript; available in PMC 2012 February 21.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscript

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set”. When the training set is small, overfitting can easily happen. Overfitting refers to asituation where the classification algorithm may perfectly classify training data, but cannotgeneralize to correctly classify new data that is not observed before. Hence, (2) we performN-fold cross-validation to minimize overfitting impacts. N-fold cross-validation refers todividing the training data into N different sets. This approach runs ANN N times, each timeusing a different set as the testing set and combining the rest N − 1 sets as the training set.Therefore, ANN is always tested with unseen data at each time. The N results from the foldsare averaged to produce a single accuracy estimation [12]. The 10-fold cross-validation isthe most commonly used method [11].III. RESULTSIn this section, we first present the result of the set of core relevant attributes returned fromthe attribute selection algorithm CFS. Then, we discuss how to refine the core attributes setand build the intelligent model.A. Core Relevant AttributesBy running CFS on the set of raw training data X (defined in (4)), we obtained a subset ofattributes,(5)The order of the attributes in C is arranged according to their relevance to skin perfusionaccording to CFS. To check whether the core set misses any other relevant attributes, we addthe remaining attributes to the core set C, one at a time. Then, we use ANN to check whetherthe inclusion of the new attribute will improve the classification accuracy.B. Construction of the Intelligent ModelBy projecting the core attributes onto the raw data set X, we obtain a core data set Xcore.Then, we train ANN to learn function f based on Xcore. As discussed before, we train andtest ANN with two different approaches, namely, “train and test with the same set” and “10-fold cross-validation”. From Table II, we can see that the learned function can correctlyclassify almost all the data (96.88%). However, overfitting does happen because theaccuracy rate for 10-fold cross-validation drops to 70.31%.Next, we gradually add attribute to the core attributes set and repeat the above experiments.By adding “gender” to the core attribute set, the accuracy rates increase substantially on“train and test with the same set” and “10-fold cross-validation”. This result suggests that“gender” should belong to the core attribute set C. Thus, we obtain a new core set C′ ={level, duration of injury, age, gender}.Next, we continue to add the remaining attributes to the new core set C′ and repeat theexperiments as above. The results show that the accuracy rates cannot be further improved.IV. DISCUSSIONThere are two purposes in this study. First of all, we demonstrate the feasibility of usingmachine-learning techniques to classify whether a given tilt and recline setting would befavorable for skin perfusion for individual wheelchair users with SCI. Specifically, we useANNs to learn the classification function f. When using function f to classify exiting data, itcan classify all the data correctly (e.g., see row “Xcore∪{gender}” in Table II). However, witha small data set, overfitting is likely to happen. The commonly used approach to minimizeFu et al.Page 5Conf Proc IEEE Eng Med Biol Soc. Author manuscript; available in PMC 2012 February 21.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscript

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overfitting impact is 10-fold cross-validation [11]. Our experimental results show that thehighest accuracy rate with 10-fold cross-validation is 75% (e.g., see row “Xcore∪{gender}” inTable II), which is still satisfying. In comparison, the accuracy rate of the traditionalmethod, i.e., the average data in each tilt and recline setting is used to perform classification,is only 59.38%. Therefore, it is desirable to use machine-learning techniques to study bloodflow response to wheelchair tilt and recline usage.The second purpose of this study is to investigate methods to construct an intelligent modelthat contains relevant attributes to skin perfusion and is able to predict favorable wheelchairtilt and recline usage for individual wheelchair users with SCI. As a start point, we use ahighly ranked attribute selection algorithm, namely, CFS [8], to obtain a core attributes set.Since attributes may interact with each other to take effect, the core attributes set may misssome relevant attributes. We gradually add the remaining attributes to the core set and see ifthe classification accuracy rates could be further improved. The experimental results showthat adding “gender” to the core attribute set substantially improves the classificationaccuracy. Therefore, “gender” is put into the core attributes set. We continue to add theremaining attributes to the new core set, however, the accuracy rates cannot be furtherimproved. Therefore, the current model includes attributes of “level of injury”, “duration ofinjury”, “age”, and “gender”, which will be validated by more participants in the subsequentstudy.V. ConclusionIn summary, the use of machine-learning techniques is promising in building an intelligentmodel that considers the correlations among different factors. The function f learned byusing ANN significantly outperforms traditional statistical approach in accuratelyclassifying favorable wheelchair tilt and recline settings.Our long-term goal is to construct a comprehensive model that considers demographic,neurological, and medical factors that are relevant to pressure ulcers. Besides classifyingwhether a given tilt and recline setting will increase skin perfusion for a wheelchair userwith SCI, the intelligent model will also predict (1) the optimal tilt and recline setting thatincreases skin perfusion the most; and (2) the optimal duration and frequency to perform tiltand recline to effectively reduce pressure ulcers risk.In addition, we will set up a Web site to make the intelligent model publicly available.People with SCI will simply input some information, such as age, gender, level, duration ofinjury, etc., then the system will provide suggestions on favorable/optimal tilt and reclinesettings for them. Therefore, our system will truly aid people with SCI to have a healthiertomorrow.AcknowledgmentsThis work was supported in part by the National Institutes of Health (NIH) (R03HD060751).References1. Alba, E.; Chicano, JF. Training neural networks with GA hybrid algorithms. In: KD, et al., editors.Proceedings of the Genetic and Evolutionary Computation Conference—GECCO 2004, ser. LectureNotes in Computer Science. Vol. 3102. Springer Verlag; Berlin, Germany: 2004. p. 852-863.2. Bliss MR. Hyperaemia. J Tissue Viability. 1998; 8:4–13. [PubMed: 10480965]3. Byrne DW, Salzberg CA. Major risk factors for pressure ulcers in the spinal cord disabled: aliterature review. Spinal Cord. 1996; 34:255–63. [PubMed: 8963971]Fu et al.Page 6Conf Proc IEEE Eng Med Biol Soc. Author manuscript; available in PMC 2012 February 21.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscript